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Linear and nonlinear unmixing in hyperspectral imaging

Abstract : This chapter introduced spectral unmixing as a powerful analysis tool able to reveal latent and unobserved spectral and spatial structures in hyperspectral images acquired through various modalities, from long-range remote sensors to microscopy imagers. By identifying the spectral signatures of the main components present in the imaged scene while quantifying their respective spatial distributions over the scene, SU provides a compact, comprehensive and meaningful (i.e., physically interpretable) description of the whole set of measurements. In an unsupervised scenario, i.e., when both the endmember spectra and abundance vectors are unknown, SU can be formulated as a blind source separation problem.
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Contributor : Saïd Moussaoui Connect in order to contact the contributor
Submitted on : Wednesday, May 6, 2020 - 6:16:10 AM
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Nicolas Dobigeon, Yoann Altmann, Nathalie Brun, Saïd Moussaoui. Linear and nonlinear unmixing in hyperspectral imaging. Data Handling in Science and Technology, Resolving Spectral Mixtures with Applications from Ultrafast Time-Resolved Spectroscopy to Super-Resolution Imaging, 30 - 2016 (Chapter 6), pp.185-224, 2016, Data Handling in Science and Technology, 978-0-444-63638-6. ⟨10.1016/B978-0-444-63638-6.00006-1⟩. ⟨hal-02564834⟩



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